scholarly journals The effect of mobility restrictions on the SARS-CoV-2 diffusion during the first wave: what are the impacts in Sweden, USA, France and Colombia

2021 ◽  
Author(s):  
Telle Olivier ◽  
Samuel Benkimoun ◽  
Richard Paul
Keyword(s):  
Social Media ◽  
Human Mobility ◽  
Great Majority ◽  
Control Measures ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Usa ◽  
Administrative Units ◽  
Individual Scale ◽  
The Individual

ResuméCombined with sanitation and social distancing measures, control of human mobility has quickly been targeted as a major leverage to contain the spread of SARS-CoV-2 in a great majority of countries worldwide. The extent to which such measures were successful, however, is uncertain (Gibbs et al. 2020; Kraemer et al. 2020). Very few studies are quantifying the relation between mobility, lockdown strategies and the diffusion of the virus in different countries. Using the anonymised data collected by one of the major social media platforms (Facebook) combined with spatial and temporal Covid-19 data, the objective of this research is to understand how mobility patterns and SARS-CoV-2 diffusion during the first wave are connected in four different countries: the west coast of the USA, Colombia, Sweden and France. Our analyses suggest a relatively modest impact of lockdown on the spread of the virus at the national scale. Despite a varying impact of lockdown on mobility reduction in these countries (83% in France and Colombia, 55% in USA, 10% in Sweden), no country successfully implemented control measures to stem the spread of the virus. As observed in Hubei (Chinazzi et al. 2020), it is likely that the virus had already spread very widely prior to lockdown; the number of affected administrative units in all countries was already very high at the time of lockdown despite the low testing levels. The second conclusion is that the integration of mobility data considerably improved the epidemiological model (as revealed by the QAIC). If inter-individual contact is a fundamental element in the study of the spread of infectious diseases, it is also the case at the level of administrative units. However, this relational dimension is little understood beyond the individual scale mostly due to the lack of mobility data at this scale. Fortunately, these types of data are getting increasingly provided by social media or mobile operators, and they can be used to help administrations to observe changes in movement patterns and/or to better locate where to implement disease control measures such as vaccination (Pollina & Busvine 2020; Pullano et al. 2020; Romm et al. 2020).

scholarly journals The effect of mobility restrictions on the SARS-CoV-2 diffusion: lessons from the first wave in Sweden, USA, France and Colombia.

2021 ◽  
Author(s):  
Olivier Telle ◽  
Richard Paul ◽  
Samuel Benkimoun
Keyword(s):  
Social Media ◽  
Real Time ◽  
West Coast ◽  
Human Mobility ◽  
Great Majority ◽  
Mobility Patterns ◽  
The West ◽  
The Usa ◽  
Social Media Platforms ◽  
Vaccination Campaigns

Abstract The control of human mobility has quickly been targeted as a major leverage to contain the spread of SARS-CoV-2 in a great majority of countries worldwide. Using more than 151 million anonymized movements registered by one of the major social media platforms combined with spatial and temporal Covid-19 data, the objective of this research is to understand how mobility patterns and SARS-CoV-2 diffusion during the first wave are connected in four different countries: the west coast of the USA, Colombia, Sweden and France. In this research, we could demonstrate that introducing movement improved considerably the model. It underlines as well that mobility played an important role in the diffusion of SARS-CoV-2, even during the lockdown, suggesting a moderate impact of lockdown on virus diffusion. However, it suggests that government could better locate where disease mitigation would be the most effective by systematically analyzing real-time movements of population. Lockdown strategies and vaccination campaigns could, for example, be prioritized according to spatial vulnerability and connectivity to hotspots of viral circulation.

scholarly journals Exploring the influence of human mobility factors and spread prediction on early COVID-19 in the USA

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhicheng Zheng ◽  
Zhixiang Xie ◽  
Yaochen Qin ◽  
Kun Wang ◽  
Yan Yu ◽  
...  
Keyword(s):  
New York ◽  
Turning Point ◽  
Early Stage ◽  
Human Mobility ◽  
The United States ◽  
Future Trend ◽  
Mobility Data ◽  
Intervention Measures ◽  
The Usa ◽  
Administrative Units

Abstract Background COVID-19 is still spreading rapidly around the world. In this context, how to accurately predict the turning point, duration and final scale of the epidemic in different countries, regions or cities is key to enabling decision makers and public health departments to formulate intervention measures and deploy resources. Methods Based on COVID-19 surveillance data and human mobility data, this study predicts the epidemic trends of national and state regional administrative units in the United States from July 27, 2020, to January 22, 2021, by constructing a SIRD model considering the factors of “lockdown” and “riot”. Results (1) The spread of the epidemic in the USA has the characteristics of geographical proximity. (2) During the lockdown period, there was a strong correlation between the number of COVID-19 infected cases and residents’ activities in recreational areas such as parks. (3) The turning point (the point of time in which active infected cases peak) of the early epidemic in the USA was predicted to occur in September. (4) Among the 10 states experiencing the most severe epidemic, New York, New Jersey, Massachusetts, Texas, Illinois, Pennsylvania and California are all predicted to meet the turning point in a concentrated period from July to September, while the turning point in Georgia is forecast to occur in December. No turning points in Florida and Arizona were foreseen for the forecast period, with the number of infected cases still set to be growing rapidly. Conclusions The model was found accurately to predict the future trend of the epidemic and can be applied to other countries. It is worth noting that in the early stage there is no vaccine or approved pharmaceutical intervention for this disease, making the fight against the pandemic reliant on non-pharmaceutical interventions. Therefore, reducing mobility, focusing on personal protection and increasing social distance remain still the most effective measures to date.

scholarly journals Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

2021 ◽  
Author(s):  
Shuhei Nomura ◽  
Yuta Tanoue ◽  
Daisuke Yoneoka ◽  
Stuart Gilmour ◽  
Takayuki Kawashima ◽  
...  
Keyword(s):  
Care Delivery ◽  
Human Mobility ◽  
Age Groups ◽  
Virus Transmission ◽  
Initial Response ◽  
Health Care Delivery System ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Status

AbstractIn the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement.

scholarly journals Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

2021 ◽  
Vol 4 ◽  
Author(s):  
A. Potgieter ◽  
I. N. Fabris-Rotelli ◽  
Z. Kimmie ◽  
N. Dudeni-Tlhone ◽  
J. P. Holloway ◽  
...  
Keyword(s):  
Human Mobility ◽  
Data Sources ◽  
Population Mobility ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The World ◽  
Spatial Weight ◽  
Data Source ◽  
Reduced Mobility ◽  
Spatial Transmission

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

scholarly journals Universal predictability of mobility patterns in cities

2014 ◽  
Vol 11 (100) ◽  
pp. 20140834 ◽  
Author(s):  
Xiao-Yong Yan ◽  
Chen Zhao ◽  
Ying Fan ◽  
Zengru Di ◽  
Wen-Xu Wang
Keyword(s):  
Spatial Scales ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Universal Prediction ◽  
Potential Applications ◽  
Mobility Behaviour ◽  
History Of ◽  
Different Characteristics ◽  
The City

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.

scholarly journals Differential effects of intervention timing on COVID-19 spread in the United States

2020 ◽  
Vol 6 (49) ◽  
pp. eabd6370 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Human Mobility ◽  
The United States ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Transmission Model ◽  
Reproductive Number ◽  
Mobility Data ◽  
Nonpharmaceutical Interventions

Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.

scholarly journals Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Kota Tsubouchi ◽  
Naoya Fujiwara ◽  
Takayuki Wada ◽  
Yoshihide Sekimoto ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Social Contact ◽  
Mobility Patterns ◽  
State Of Emergency ◽  
Social Contacts ◽  
Policy Makers ◽  
Mobility Data ◽  
Contact Rates ◽  
Mobility Behavior

Abstract While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

scholarly journals Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

scholarly journals The effect of human mobility and control measures on the COVID-19 epidemic in China

2020 ◽  
Author(s):  
Moritz U.G. Kraemer ◽  
Chia-Hung Yang ◽  
Bernardo Gutierrez ◽  
Chieh-Hsi Wu ◽  
Brennan Klein ◽  
...  
Keyword(s):  
Human Mobility ◽  
Control Measures ◽  
Human Populations ◽  
Travel History ◽  
Mobility Data ◽  
Travel Restrictions ◽  
State Interventions ◽  
And Control ◽  
The Impact

AbstractThe ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.

scholarly journals Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study (Preprint)

2020 ◽  
Author(s):  
Romain Garnier ◽  
Jan R Benetka ◽  
John Kraemer ◽  
Shweta Bansal
Keyword(s):  
United States ◽  
Black People ◽  
The United States ◽  
Control Measures ◽  
Mobility Patterns ◽  
Poverty Level ◽  
Social Distancing ◽  
Mobility Data ◽  
Black Populations ◽  
The Impact

BACKGROUND Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. OBJECTIVE We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

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